Reinforcement learning (RL) has shown promising results for real-time control systems, including the domain of plasma magnetic control. However, there are still significant drawbacks compared to traditional feedback control approaches for magnetic confinement. In this work, we address key drawbacks of the RL method; achieving higher control accuracy for desired plasma properties, reducing the steady-state error, and decreasing the required time to learn new tasks. We build on top of \cite{degrave2022magnetic}, and present algorithmic improvements to the agent architecture and training procedure. We present simulation results that show up to 65\% improvement in shape accuracy, achieve substantial reduction in the long-term bias of the plasma current, and additionally reduce the training time required to learn new tasks by a factor of 3 or more. We present new experiments using the upgraded RL-based controllers on the TCV tokamak, which validate the simulation results achieved, and point the way towards routinely achieving accurate discharges using the RL approach.

Predicting the future motion of traffic agents is vital for self-driving vehicles to ensure their safe operation. We introduce RedMotion, a transformer model for motion prediction that incorporates two types of redundancy reduction. The first type of redundancy reduction is induced by an internal transformer decoder and reduces a variable-sized set of road environment tokens, such as road graphs with agent data, to a fixed-sized embedding. The second type of redundancy reduction is a self-supervised learning objective and applies the redundancy reduction principle to embeddings generated from augmented views of road environments. Our experiments reveal that our representation learning approach can outperform PreTraM, Traj-MAE, and GraphDINO in a semi-supervised setting. Our RedMotion model achieves results that are competitive with those of Scene Transformer or MTR++. We provide an open source implementation that is accessible via GitHub and Colab. It is essential for self-driving vehicles to understand the relation between the motion of traffic agents and the surrounding road environment. Motion prediction aims to predict the future trajectory of traffic agents based on past trajectories and the given traffic scenario. Recent state-of-the-art methods (e.g., Shi et al. (2022); Wang et al. (2023); Nayakanti et al. (2023)) are deep learning methods trained using supervised learning.

This paper studies a decentralized multi-armed bandit problem in a multi-agent network. The problem is simultaneously solved by N agents assuming they face a common set of M arms and share the same arms' reward distributions. Each agent can receive information only from its neighbors, where the neighbor relationships among the agents are described by an undirected graph. Two fully decentralized multi-armed bandit algorithms are proposed, respectively based on the classic upper confidence bound (UCB) algorithm and the state-of-the-art KL-UCB algorithm. The proposed decentralized algorithms permit each agent in the network to achieve a better logarithmic asymptotic regret than their single-agent counterparts, provided that the agent has at least one neighbor, and the more neighbors an agent has, the better regret it will have, meaning that the sum is more than its component parts.

As modern systems become ever more connected with complex dynamic coupling relationships, the development of safe control methods for such networked systems becomes paramount. In this paper, we define a general networked model with coupled dynamics and local control and discuss the relationship of node-level safety definitions for individual agents with local neighborhood dynamics. We define a node-level barrier function (NBF), node-level control barrier function (NCBF), and collaborative node-level barrier function (cNCBF) and provide conditions under which sets defined by these functions will be forward invariant. We use collaborative node-level barrier functions to construct a novel distributed algorithm for the safe control of collaborating network agents and provide conditions under which the algorithm is guaranteed to converge to a viable set of safe control actions for all agents or a terminally infeasible state for at least one agent. We introduce the notion of non-compliance of network neighbors as a metric of robustness for collaborative safety for a given network state and chosen barrier function hyper-parameters. We illustrate these results on a networked susceptible-infected-susceptible (SIS) model.

This paper addresses the distributed online control problem over a network of linear time-invariant (LTI) systems (with possibly unknown dynamics) in the presence of adversarial perturbations. There exists a global network cost that is characterized by a time-varying convex function, which evolves in an adversarial manner and is sequentially and partially observed by local agents. The goal of each agent is to generate a control sequence that can compete with the best centralized control policy in hindsight, which has access to the global cost. This problem is formulated as a regret minimization. For the case of known dynamics, we propose a fully distributed disturbance feedback controller that guarantees a regret bound of $O(\sqrt{T}\log T)$, where $T$ is the time horizon. For the unknown dynamics case, we design a distributed explore-then-commit approach, where in the exploration phase all agents jointly learn the system dynamics, and in the learning phase our proposed control algorithm is applied using each agent system estimate. We establish a regret bound of $O(T^{2/3} \text{poly}(\log T))$ for this setting.

Robot swarms can be tasked with a variety of automated sensing and inspection applications in aerial, aquatic, and surface environments. In this paper, we study a simplified two-outcome surface inspection task. We task a group of robots to inspect and collectively classify a 2D surface section based on a binary pattern projected on the surface. We use a decentralized Bayesian decision-making algorithm and deploy a swarm of miniature 3-cm sized wheeled robots to inspect randomized black and white tiles of $1m\times 1m$. We first describe the model parameters that characterize our simulated environment, the robot swarm, and the inspection algorithm. We then employ a noise-resistant heuristic optimization scheme based on the Particle Swarm Optimization (PSO) using a fitness evaluation that combines decision accuracy and decision time. We use our fitness measure definition to asses the optimized parameters through 100 randomized simulations that vary surface pattern and initial robot poses. The optimized algorithm parameters show up to a 55% improvement in median of fitness evaluations against an empirically chosen parameter set.

Rather than having each newly deployed robot create its own map of its surroundings, the growing availability of SLAM-enabled devices provides the option of simply localizing in a map of another robot or device. In cases such as multi-robot or human-robot collaboration, localizing all agents in the same map is even necessary. However, localizing e.g. a ground robot in the map of a drone or head-mounted MR headset presents unique challenges due to viewpoint changes. This work investigates how active visual localization can be used to overcome such challenges of viewpoint changes. Specifically, we focus on the problem of selecting the optimal viewpoint at a given location. We compare existing approaches in the literature with additional proposed baselines and propose a novel data-driven approach. The result demonstrates the superior performance of the data-driven approach when compared to existing methods, both in controlled simulation experiments and real-world deployment.

In multiagent systems (MASs), agents' observation upon system behaviours may improve the overall team performance, but may also leak sensitive information to an observer. A quantified observability analysis can thus be useful to assist decision-making in MASs by operators seeking to optimise the relationship between performance effectiveness and information exposure through observations in practice. This paper presents a novel approach to quantitatively analysing the observability properties in MASs. The concept of opacity is applied to formally express the characterisation of observability in MASs modelled as partially observable multiagent systems. We propose a temporal logic oPATL to reason about agents' observability with quantitative goals, which capture the probability of information transparency of system behaviours to an observer, and develop verification techniques for quantitatively analysing such properties. We implement the approach as an extension of the PRISM model checker, and illustrate its applicability via several examples.

Ensuring safety in dynamic multi-agent systems is challenging due to limited information about the other agents. Control Barrier Functions (CBFs) are showing promise for safety assurance but current methods make strong assumptions about other agents and often rely on manual tuning to balance safety, feasibility, and performance. In this work, we delve into the problem of adaptive safe learning for multi-agent systems with CBF. We show how emergent behavior can be profoundly influenced by the CBF configuration, highlighting the necessity for a responsive and dynamic approach to CBF design. We present ASRL, a novel adaptive safe RL framework, to fully automate the optimization of policy and CBF coefficients, to enhance safety and long-term performance through reinforcement learning. By directly interacting with the other agents, ASRL learns to cope with diverse agent behaviours and maintains the cost violations below a desired limit. We evaluate ASRL in a multi-robot system and a competitive multi-agent racing scenario, against learning-based and control-theoretic approaches. We empirically demonstrate the efficacy and flexibility of ASRL, and assess generalization and scalability to out-of-distribution scenarios. Code and supplementary material are public online.

Recent challenges in operating power networks arise from increasing energy demands and unpredictable renewable sources like wind and solar. While reinforcement learning (RL) shows promise in managing these networks, through topological actions like bus and line switching, efficiently handling large action spaces as networks grow is crucial. This paper presents a hierarchical multi-agent reinforcement learning (MARL) framework tailored for these expansive action spaces, leveraging the power grid's inherent hierarchical nature. Experimental results indicate the MARL framework's competitive performance with single-agent RL methods. We also compare different RL algorithms for lower-level agents alongside different policies for higher-order agents.